In this section, we a apply a reinforcement learning algorithm for motion imitation by using the online distance measure between embodiments as a feedback signal. Reinforcement learning has shown great success in learning motor tasks, for example, [1]}, [2]}. We study the transfer of motions from one Panda robot with maximally seven DOFs to another one in simulation. As before, different embodiments are generated by locking DOFs in the learner.
We assume that the dynamics of the robots are unavailable (model-free) and that the learner is controlled by joint torques \(\mathbf {\tau }\) . Consequently, the agent needs to control the joint positions but also, implicitly, learn the the robot dynamics.
